Assuming we are able to predict good candidates for contrastive focus, it remains to be verified if we can control the contrastive prominence on pronouns in a neural TTS system. Other works have proposed utterance-level control systems [1]}, [2]} but for this task we need to target the word-level. This was achieved previously within an HMM framework [3]}.
[4]} evaluated the expression of prosodic features from ToBI-label conditioned TTS, but only for ground-truth input labels.
[5]} report successful control over other word categories from systems conditioned on human or automatically annotated data (although this was not evaluated with a perceptual test). More recently, [6]} proposed to use control tags for prominence in an end-to-end TTS system. However, their system relied on the availability of a specific (and limited) pre-annotated corpus whereas we demonstrate controllability from prominence labels obtained automatically.
